Document Clustering using K-Medoids
This addresses the challenge of information overload for internet users, but it is incremental as it applies an existing method to document data.
The paper tackles the problem of organizing large internet data for easier access by clustering similar documents, using the K-Medoids algorithm for document summarization.
People are always in search of matters for which they are prone to use internet, but again it has huge assemblage of data due to which it becomes difficult for the reader to get the most accurate data. To make it easier for people to gather accurate data, similar information has to be clustered at one place. There are many algorithms used for clustering of relevant information in one platform. In this paper, K-Medoids clustering algorithm has been employed for formation of clusters which is further used for document summarization.